We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypotheses for sequence composition through calculation of likelihood or deviation evidence from the comparison of predictions from the hypothesized sequence with target trace data. We machine learn a means for comparing the measures taken from competing hypotheses for the sequence. This is a machine learned implementation of our proposal for abductive DNA basecalling. The results of the present experiments suggest that neural nets are a more effective means for predicting peak sizes than decision tree regressors, and for assembling evidence for competing hypotheses in this context. This is despite the availability of variance estimates in our decision tree regressors.
The new coronavirus infection (COVID-19) represents a challenge for global health. Since the outbreak began, the number of confirmed cases has exceeded 117 million, with more than 2.6 million deaths worldwide. With public health measures aimed at containing the spread of the disease, several countries have faced a crisis in the availability of intensive care units. Currently, a large-scale effort is underway to identify the nucleotide sequences of the SARS-CoV-2 coronavirus that is an etiological agent of COVID-19. Global sequencing of thousands of viral genomes has revealed many common genetic variants, which enables the monitoring of the evolution of SARS-CoV-2 and the tracking of its spread over time. Understanding the current evolution of SARS-CoV-2 is necessary not only for a retrospective analysis of the new coronavirus infection spread, but also for the development of approaches to the therapy and prophylaxis of COVID-19. In this review, we have focused on the general characteristics of SARS-CoV-2 and COVID-19. Also, we have analyzed available publications on the genetic diversity of the virus and the relationship between the diversity and the biological properties of SARS-CoV-2, such as virulence and contagiousness.
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